Saturday, February 13, 2016

Recently, Sergey, a Junior Researcher at the ISSP RAS contacted me and told me that he found a way to make PIVlab (specifically the DFT window deformation part) much faster. He rewrote PIV_FFTmulti.m in a way that doesn't use 'for loops' anymore. The result is a heavily improved processing speed of PIVlab. I tested MATLAB versions 2011a, 2014b and 2015a. The speed was improved by a factor between 7.9 and 10.6 for a 'standard analysis' with three passes. Improvements up to a factor of 30 seem possible.

Monday, March 16, 2015

I am making progress with the r2014 update of PIVlab, several incompatibilities are fixed already. This requires some extra attention, because PIVlab must stay compatible with earlier Matlab releases. I think that I will be ready to post a beta release of PIVlab 1.4 at the end of this week.

Wednesday, January 21, 2015

Hey, we just reached the goal of 134 Euros! Thanks! I just downloaded MATLAB and will soon start working on PIVlab v1.4 ! I already noticed that R2014b is quite fast in comparison to previous releases.

Sunday, January 11, 2015

Mathworks changed a lot in their latest Matlab release (R2014b), making it incompatible with PIVlab 1.35 (and probably many other toolboxes). I'd like to make it compatible, but I need access to Matlab R2014b to do the necessary changes and to find all the issues.

I already contacted Mathworks, but they are not willing to support me with an extended trial version. I am not working at the university anymore, therefore I don't have access to any academic licenses.

A "Home license" of Matlab + Image processing toolbox is 134 euros.

If you're interested in this update, then please donate as much or as little as you like. But please note that your donation does not guarantee that I will succeed in making the necessary changes. You do not receive any additional rights. Everything I do is just voluntary - just like your donation.

If I manage to receive 134 Euros until the 15. march 2015, I'll buy the license and start trying to make PIVlab compatible. If I do not manage to receive 134 Euros in total, then you will get your money back (excluding the paypal fee... sorry).

I will update the current status of the donations as often as possible in this post.

Click the Paypal button to make your donation...:

Thanks, let's see what happens...!
William

--UPDATE: The goal of 134 Euros was reached on the 04. february 2014. I am now working on the patch.

Thursday, October 23, 2014

Hi, due to the increasing amount of PIVlab users and questions, I enabled a forum in this blog where questions can be asked. I am hoping that this can act as a central place for all comments and questions and that I can reply faster to questions without forgetting some of them...
I disabled the comments temporarily to see if the forum could work....

Thursday, November 24, 2011

Soon, I will need to analyze a large dataset (441.000 image pairs), so I did some tests to speed up the data processing. When using a QuadCore computer (actually 8 virtual cores in this case), up to eight Matlab instances can run in parallel to increase speed. Running more instances will result in a decrease in speed.
A similar effect is also possible with a DualCore computer. Running two Matlab instances in parallel increases the speed by almost 100%.
If you also need to save time, you might try to split your data and run as many Matlab instances as there are (virtual) CPU cores on your computer. Starting the PIV analysis from the commandline (see example in PIVlab_commandline.m), and starting Matlab with the "-nodesktop" command will additionally increase speed.

Processing time for the analysis of one large image pair with multiple passes on different systems

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